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single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related
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applications for a post-doctoral research position under the supervision of Dr. Chris Smith Home | Chris Smith . The lab— in the Evolution, Ecology, and Behavior section—investigates machine learning approaches
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. This fellowship requires in-person participation in Manhattan, Kansas. Learning objectives: During this appointment, you will have the opportunity to: Gain experience in sorting and identifying insects of medical
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Ph.D. in neurobiology or a related field. 1 year post-doctoral experience Certificates/Credentials/Licenses Applicants must have a Ph.D. in neurobiology or a related field Computer Skills
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denoising, cell segmentation and the analysis of cellular neighbourhoods and cell–cell interactions. Experience in developing artificial intelligence and machine-learning methodologies for multimodal data
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. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern machine learning methods as well as in biological data analysis are needed for the position
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interdisciplinary research teams on quantitative analyses of complex genomic datasets; Learn to use remote, high powered computer clusters to process large datasets. Mentor: The mentor for this opportunity is Adam
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for cell transplantation therapies in animal models of Alzheimer's disease, stroke, and epilepsy. To achieve this goal, the candidate will combine a gene network-based approach with a machine learning model
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling